Bottom-up synthesis of recursive functional programs using angelic execution

We present a novel bottom-up method for the synthesis of functional recursive programs. While bottom-up synthesis techniques can work better than top-down methods in certain settings, there is no prior technique for synthesizing recursive programs from logical specifications in a purely bottom-up fashion. The main challenge is that effective bottom-up methods need to execute sub-expressions of the code being synthesized, but it is impossible to execute a recursive subexpression of a program that has not been fully constructed yet. In this paper, we address this challenge using the concept of angelic semantics. Specifically, our method finds a program that satisfies the specification under angelic semantics (we refer to this as angelic synthesis), analyzes the assumptions made during its angelic execution, uses this analysis to strengthen the specification, and finally reattempts synthesis with the strengthened specification. Our proposed angelic synthesis algorithm is based on version space learning and therefore deals effectively with many incremental synthesis calls made during the overall algorithm. We have implemented this approach in a prototype called Burst and evaluate it on synthesis problems from prior work. Our experiments show that Burst is able to synthesize a solution to 94% of the benchmarks in our benchmark suite, outperforming prior work.

A system for deduction-based formal verification of workflow-oriented software models

The work concerns formal verification of workflow-oriented software models using the deductive approach. The formal correctness of a model’s behaviour is considered. Manually building logical specifications, which are regarded as a set of temporal logic formulas, seems to be a significant obstacle for an inexperienced user when applying the deductive approach. A system, along with its architecture, for deduction-based verification of workflow-oriented models is proposed. The process inference is based on the semantic tableaux method, which has some advantages when compared with traditional deduction strategies. The algorithm for automatic generation of logical specifications is proposed. The generation procedure is based on predefined workflow patterns for BPMN, which is a standard and dominant notation for the modeling of business processes. The main idea behind the approach is to consider patterns, defined in terms of temporal logic, as a kind of (logical) primitives which enable the transformation of models to temporal logic formulas constituting a logical specification. Automation of the generation process is crucial for bridging the gap between the intuitiveness of deductive reasoning and the difficulty of its practical application when logical specifications are built manually. This approach has gone some way towards supporting, hopefully enhancing, our understanding of deduction-based formal verification of workflow-oriented models.